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Exergy Life Cycle Assessment (ELCA) is proposed by literature to account for the exergy embodied in products of energy systems. In order to make results of ELCA comparable, supply chains that support the life cycle of the system should be analyzed through a unified model: this is one of the main concerns related to Life Cycle Assessment and Industrial Ecology disciplines. In this paper, Hybrid Input-Output analysis is proposed as the computational structure of ELCA: according to this method, national supply chains are modeled through the Monetary Input Output Tables (MIOTs) of national economies, a constantly updated and freely available data source. Then, the adopted national MIOT is expanded to include the detailed model of the considered energy system, hence defining a Hybrid Input-Output model. The (non-renewable) exergy embodied in electricity production and the Exergy Return on Investment (ExROI) are defined as the appropriate performance indicators based on ELCA. The introduced model is here adopted for the analysis of a Waste-to-Energy (WtE) power plant currently operating in the Italian context. It is found that the primary non-renewable exergy embodied in electricity produced by the analyzed WtE is non-negligible for both the construction (127.1 toe) and the operation phases (11.6 toe/y). Nonetheless, the plant is able to produce a net amount of electricity that pays back such primary non-renewable resources requirements about a hundred times. Finally, the joint application of Exergy Analysis and ELCA lead to improve the overall thermodynamic performances of the WtE system, increasing its exergy efficiency by 1%, and reducing the non-renewable exergy embodied in electricity production by 7938 toe. [ABSTRACT FROM AUTHOR]

The coexistence of the need to improve economic conditions and the conscious use of environmental resources plays a central role in today's sustainable development challenge. In this study, a novel integrated framework to evaluate the impact of new technological interventions is presented and an application to smallholder coffee farms and their supply chains in Kenya is proposed. This methodology is able to combine multiple information through the joint use of three approaches: supply chain analysis, input-output analysis, and energy system modeling. Application to the context of the Kenyan coffee sector enables framework validation: shading management measures, the introduction of eco-pulpers, and the exploitation of coffee waste biomass for power generation were compared within a holistic high-level perspective. The implementation of shading practices, carried out with fruit trees, shows the most relevant effects from the economic point of view, providing farmers with an additional source of income and generating $903 of work for every million of local currency (about $9k) invested in this solution. The same investment would save up to 1.46 M m3 of water per year with the eco-pulpers technology. Investing the same amount in coffee-biomass power plants would displace a small portion of production from heavy-duty oil and avoid importing a portion of fertilizer, saving up to 11 tons of CO2 and around $4k per year. The results suggest the optimal allocation of a $100m budget, which can be affected by adding additional constraints on minimum environmental or social targets in line with sustainable development goals. [ABSTRACT FROM AUTHOR]